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Data Poisoning

Data Poisoning is an adversarial attack that tries to manipulate the training dataset in order to control the prediction behavior of a trained model such that the model will label malicious examples into a desired classes (e.g., labeling spam e-mails as safe).

Source: Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

Papers

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Showing 231–240 of 492 papers

TitleStatusHype
Balancing Privacy, Robustness, and Efficiency in Machine Learning—0
Progressive Poisoned Data Isolation for Training-time Backdoor DefenseCode0
TrojFSP: Trojan Insertion in Few-shot Prompt Tuning—0
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey—0
Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks—0
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks—0
Mendata: A Framework to Purify Manipulated Training Data—0
Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective—0
Universal Backdoor AttacksCode0
Trainwreck: A damaging adversarial attack on image classifiersCode0
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